Statistical modelling of extreme precipitation indices for the Mediterranean area under future climate change

摘要

Projected changes of extreme precipitation in the Mediterranean area up until the end of the 21st century are analysed by means of statistical downscaling. Generalized linear models are used as downscaling technique to assess different percentile-based indices of extreme precipitation on a fine-scale spatial resolution. In the region under consideration extreme precipitation is related to anomalies of the large-scale circulation as well as to convective conditions. To account for this, predictor selection encompasses variables describing the large-scale circulation (geopotential heights of the 700 hPa and 500 hPa levels, u- and v-wind components of the 850 hPa level) as well as thermo-dynamic parameters (specific humidity of the 850 hPa and 700 hPa levels, Showalter-Index, convective inhibition). In the scope of the statistical downscaling approach a specific statistical ensemble technique is applied in order to allow for non-stationarities in the predictors-predictand relationships. Consequently, the statistical ensembles include a range of possible future evolutions of extreme precipitation. Two different emission scenarios (A1B and B1), multiple runs for each scenario, and output of two different general circulation models (ECHAM5 and HadCM3) are applied to assess extreme precipitation under enhanced greenhouse warming conditions. The results yield mainly decreases over many parts of the Mediterranean area in spring. In summer increases are assessed around the Tyrrhenian Sea, the Ionian Sea, and the Aegean Sea, whereas decreases are projected for most of the western and northern Mediterranean regions. In autumn reductions of heavy rainfall occur over many parts of the western and central areas. In winter distinct increases are widespread in the Mediterranean area. Beyond the assessments using all predictors it is shown in the present contribution that different predictor variables can lead to varying statistical downscaling results. It points to distinct impacts of the change of specific atmospheric conditions on local extreme precipitation.